Connected vehicles generate a vast amount of data and share it with external entities such as the cloud, neighboring vehicles, Road-Side Units (RSUs), and other third-party services in a Vehicle to Everything (V2X) setting. This data is vulnerable and can lead to the leakage of personal information of vehicle owners, such as driving habits, travel routes, and identity theft, among others. Moreover, with the implementation of the General Data Protection Regulation (GDPR), it becomes imperative to empower users with control over their data and the ability to choose whom they share it with. To address this objective, we present a protocol that utilizes a combination of sticky policies and a proxy re-encryption scheme. This protocol ensures that user-defined access controls on the data persist even when crossing organizational boundaries and addresses the confidentiality, integrity, and accountability of vehicle data. Furthermore, we assess our protocol using a semi-honest threat model and analyze its vulnerabilities. Lastly, we perform a quantitative analysis of the data flow model to observe the system's performance.

Artificial intelligence systems such as Large Language Models (LLM) derive their knowledge from large datasets. Systems like ChatGPT therefore rely on shared data to train on. For companies, releasing data to the public domain requires anonymization as soon as an individual is identifiable. While there are several privacy models that guarantee a certain level of distortion applied to a dataset, to mitigate re-identification, e.g. with k-anonymity, the required level is generally defined by the data processor. We propose the idea of combining individual privacy levels defined by the data subjects themselves with a privacy language, such as the Layered Privacy Language (LPL) [10], to get a more fine-grained understanding of the effectively required privacy level. Queries that target subsets of the dataset to be released can only benefit from lower privacy requirements set by data subjects, as these response subsets may do not contain users with high privacy requirements, which can then lead to more utility. By analyzing the results of different queries to a privacy-aware data-transforming database system, we demonstrate the characteristics required for this assumption to be truly effective. For a more realistic evaluation, we also consider changes in the underlying data sources.

The increasing digitalization in manufacturing and trends like the Industrial Metaverse drive the need for secure decentralized asset exchanges and industrial asset intellectual property (IP) rights protection. Utilizing distributed ledgers and non-fungible tokens (NFTs) linked by a hash enables the management and authentication of assets. However, challenges arise in authenticating the same asset’s different versions and file formats using a single hash.In this work, we propose a new NFT structure that allows binding multiple files existing in different file formats, representing the same digital asset by combining a Merkle Tree (MT) with a calculated Average Perceptual Hash (APH). While the MT proves bitwise integrity, the APH verifies functional integrity for asset authentication. We evaluate our approach for exchanging Printed Circuit Board (PCB) designs in a decentralized ecosystem by calculating unique PCB fingerprints and the APH. Our results show a robust identification and integrity verification of PCB designs depending on the manipulation type. Our approach for asset authentication is generalizable to all asset classes with an appropriate perceptual hash.

The overweight and obesity rate is increasing for decades worldwide. Healthy nutrition is, besides education and physical activity, one of the various keys to tackle this issue. In an effort to increase the availability of digital, healthy recommendations, the scientific area of food recommendation extends its focus from the accuracy of the recommendations to beyond-accuracy goals like transparency and healthiness. To address this issue a data basis is required, which in the ideal case encompasses user-item interactions like ratings and reviews, food-related information such as recipe details, nutritional data, and in the best case additional data which describes the food items and their relations semantically. Though several recipe recommendation data sets exist, to the best of our knowledge, a holistic large-scale healthiness-aware and connected data sets have not been made available yet. The lack of such data could partially explain the poor popularity of the topic of healthy food recommendation when compared to the domain of movie recommendation. In this paper, we show that taking into account only user-item interactions is not sufficient for a recommendation. To close this gap, we propose a connected data set called HUMMUS (Health-aware User-centered recoMMendation and argUment-enabling data Set) collected from Food.com containing multiple features including rich nutrient information, text reviews, and ratings, enriched by the authors with extra features such as Nutri-scores and connections to semantic data like the FoodKG and the FoodOn ontology. We hope that these data will contribute to the healthy food recommendation domain.

While convolutional neural networks are dominating the field of computer vision, one usually does not have access to the large amount of domain-relevant data needed for their training. Therefore, it has become common practice to use available synthetic samples along domain adaptation schemes to prepare algorithms for the target domain. Tackling this problem from a different angle, we introduce a pipeline to map unseen target samples into the synthetic domain used to train task-specific methods. Denoising the data and retaining only the features these recognition algorithms are familiar with, our solution greatly improves their performance. As this mapping is easier to learn than the opposite one (i.e., to generate realistic features to augment the source samples), we demonstrate how our whole solution can be trained purely on augmented synthetic data and still performs better than methods trained with domain-relevant information (e.g., real images or realistic textures for the 3D models). Applying our approach to object recognition from texture-less CAD data, we present a custom generative network which fully utilizes the purely geometrical information to learn robust features and to achieve a more refined mapping for unseen color images.

Over the last decade, pedestrian navigation services are gaining a high interest in guiding and tracking the user at any place. Outdoor, GPS persists as the navigation standard. Nevertheless, indoor navigation services, mainly developed to meet the need of a single floor building, are still under development stage. This paper introduces a novel algorithm that computes and displays the pathway, in a continuous manner, between two different points (i.e. source point and destination point) located inside a multi-floor building or outdoor. The calculated pathway is composed of two parts; the first part is displayed in the source floor and the second part is viewed in the destination floor. In cases where the source point and the destination point are in different floors and after displaying the first part of the pathway, the proposed algorithm automatically detects if the user is close to the destination floor based on the difference of altitudes. Therefore, the first part of the pathway is dropped and the second part is displayed. We conducted several real experiments, inside the campus of the University of Passau in Germany, to evaluate the performance of the proposed algorithm. They yielded promising results in terms of continuity of navigation service and we achieved an accuracy in estimating the floor equal to 0.093 m.

We present a method to incrementally generate complete 2D or 3D scenes with the following properties: (a) it is globally consistent at each step according to a learned scene prior, (b) real observations of a scene can be incorporated while observing global consistency, (c) unobserved regions can be hallucinated locally in consistence with previous observations, hallucinations and global priors, and (d) hallucinations are statistical in nature, i.e., different scenes can be generated from the same observations. To achieve this, we model the virtual scene, where an active agent at each step can either perceive an observed part of the scene or generate a local hallucination. The latter can be interpreted as the agent's expectation at this step through the scene and can be applied to autonomous navigation. In the limit of observing real data at each point, our method converges to solving the SLAM problem. It can otherwise sample entirely imagined scenes from prior distributions. Besides autonomous agents, applications include problems where large data is required for building robust real-world applications, but few samples are available. We demonstrate efficacy on various 2D as well as 3D data.

With the increasing availability of large databases of 3D CAD models, methods for depth-based recognition of localized objects can be trained on an uncountable number of synthetically rendered images. However, discrepancies with the real data acquired from various depth sensors still noticeably impede progress. Previous works adopted unsupervised approaches to generate more realistic depth data, but they all require real scans for training, even if unlabeled. This still represents a strong requirement, especially when considering real-life/industrial settings where real training images are hard or impossible to acquire, but texture-less 3D models are available. We thus propose a novel approach leveraging only CAD models to bridge the realism gap. Purely trained on synthetic data, playing against an extensive augmentation pipeline in an unsupervised manner, our generative adversarial network learns to effectively segment depth images and recover the clean synthetic-looking depth information even from partial occlusions. As our solution is not only fully decoupled from the real domains but also from the task-specific analytics, the pre-processed scans can be handed to any kind and number of recognition methods also trained on synthetic data. Through various experiments, we demonstrate how this simplifies their training and consistently enhances their performance, with results on par with the same methods trained on real data, and better than usual approaches doing the reverse mapping.

With the advent of wearable and smartphone technologies, the need for pedestrian navigation services has increased over the last decade. Outdoor, the GPS remains currently the navigation standard. However, indoor navigation is in a development stage and suffers from many limitations, mainly related to the used positioning technologies and techniques and to the problem of finding the path from the origin to the destination. Previous studies have been limited to either positioning services or navigation services within a single floor building. The focus of this work is to develop a multi-floor pedestrian navigation service. This prototype is composed of two main modules. First, a positioning module, is conceived to compute the mobile unit (MU) position using the pedestrian dead reckoning (PDR) and the received signal strength indicator (RSSI) fingerprinting positioning techniques. Second, a navigation module, is designed to identify the rooms' numbers inside a building and to generate the graph for the whole building. The performance of our prototype is evaluated in the building IT Zentrum/International House on the campus of the University of Passau in Germany.

Recent progress in computer vision has been dominated by deep neural networks trained over larges amount of labeled data. Collecting such datasets is however a tedious, often impossible task; hence a surge in approaches relying solely on synthetic data for their training. For depth images however, discrepancies with real scans still noticeably affect the end performance. We thus propose an end-to-end framework which simulates the whole mechanism of these devices, generating realistic depth data from 3D models by comprehensively modeling vital factors e.g. sensor noise, material reflectance, surface geometry. Not only does our solution cover a wider range of sensors and achieve more realistic results than previous methods, assessed through extended evaluation, but we go further by measuring the impact on the training of neural networks for various recognition tasks; demonstrating how our pipeline seamlessly integrates such architectures and consistently enhances their performance.

Crowdsourcing has emerged as a promising approach for obtaining services and data in a short time and at a reasonable budget. However, the quality of the output provided by the crowd is not guaranteed, and must be controlled. This quality control usually relies on worker screening or contribution reviewing at the cost of additional time and budget overheads. In this paper, we propose to reduce these overheads by leveraging the system history. We describe an offline learning algorithm that groups tasks from history into homogeneous clusters and learns for each cluster the worker features that optimize the contribution quality. These features are then used by the online targeting algorithm to select reliable workers for each incoming task. The proposed method is compared to the state of the art selection methods using real world datasets. Results show that we achieve comparable, and in some cases better, output quality for a smaller budget and shorter time.

The quality of the singing voice is an important aspect of subjective, aesthetic perception of music. In this contribution, we propose a method to automatically assess perceived singing quality. We classify monophonic vocal recordings without accompaniment into one of three classes of singing quality. Unprocessed private and non-commercial recordings from a social media website are utilised. In addition to the user ratings given on the website, we let both subjects with and without a musical background annotate the samples. Building on musicological foundations, we define and extract acoustic parameters describing the quality of the sound, musical expression and intonation of the singing. Besides features which are already established in the field of Music Information Retrieval, such as loudness and mel-frequency cepstral coefficients, we propose and employ new types of features which are specific to intonation. For automatic classification by supervised machine learning methods, models predicting the subjective ratings and the user ratings on the social media website are learnt. We perform an exhaustive evaluation of both different classifiers and combinations of features. We show that the performance of automatic classification is close to that of human evaluators. Utilising support vector machines, an accuracy of classification of 55.4 %, based on the subjective ratings, and of 84.7 %, based on the user ratings of the social media website, are achieved.

Quite recently, considerable attention has been paid to pedestrian navigation services, since they provide people with guidance and tracking information everywhere especially in an unfamiliar environment. In the literature, indoor and outdoor navigation has been widely investigated. However, studies on the continuity of pedestrian navigation service, still lacking. This paper presents a novel positioning technology selection algorithm able to integrate the pedestrian dead reckoning (PDR) technique, for short-term, through the navigation process either to improve the calculation of the mobile unit's (MU) position and/or to maintain the continuity of the service. The integration of the PDR is inferred based on the fuzzy logic approach. In fact, the proposed algorithm defines a fuzzy variable called “margin” which identifies four different states, namely, confidence, intermediate, confusion and critic. The PDR technique is only incorporated in the last two states in two ways: either fused with the used radio frequency (RF) positioning technology or stand-alone if no RF technology is available. The algorithm performance is evaluated based on an empirical study.

Opportunistic networks suffer some coarse characteristics as frequent disruptions, high loss ratios and uncertain data delivery. The transmission of video content even worsens the delivery problem because of the large size and continuous nature of the medium. Therefore, loss concealment is inducted as a part of the solution, alongside other delivery enhancement mechanisms. Loss concealment techniques are usually used Forwards, where the source must spend extra processing and transmission volume to enable the receiver to recover lost data parts. These techniques are not only associated with prolonged latencies and video encoding manipulations, but they are also sensitive to the loss pattern, which might leave the receiver in passive suspension if a key part for recovery was missing. In this work, we propose a Backward loss concealment mechanism, which allows the receiver to react to a certain amount of loss. Missing parts of the scalable video stream are compensated by a composite solution that encloses 1) video frame loss error concealment, and 2) network demands, which are initiated and cast in search for missing parts from other nodes in the network. Simulation-driven experiments prove the applicability of the backward mechanism, with a limited capability depending on the target application requirements.

The growing number of elderly people combined with financial cuts in the health care sector lead to an increased demand for computer supported medical services. New standards like HTML5 allow the creation of hypervideo training applications that run on a variety of end user devices. In this paper, we evaluate an HTML5 player running an e-health hypervideo for the support of pelvic floor exercises. In an experimental test setting we compared the hypervideo to a primarily linear version regarding usability and utilization for self-controlled training. Our results show the hypervideo version leads to slightly more usability problems but facilitated a more active and individual training.

We report on our experience with enhancing the data-management component in the avionics software of the NH90 helicopter at Airbus Helicopters. We describe challenges regarding the evolution of avionics software by means of real-world evolution scenarios that arise in industrial practice. A key role plays a legally-binding certification process, called qualification, which is responsible for most of the development effort and cost. To reduce effort and cost, we propose a novel generative approach to develop qualifiable avionics software by combining model-based and product-line technology. Using this approach, we have already generated code that is running on the NH90 helicopter and that is in the process of replacing the current system code. Based on an interview with two professional developers at Airbus and an analysis of the software repository of the NH90, we systematically compare our approach with established development approaches in the avionics domain, in terms of implementation and qualification effort.

Video transmission in opportunistic networks is very challenging due to the network coarse characteristics of frequent disruptions and uncertain data delivery. This is on the one hand, and the large size and continuous nature of videos on the other hand. In a previous work of Klaghstan et. al (2013), they presented a solution based on Scalable Video Coding (SVC), which aims at improving the user viewing experience assessed by means of playout delay and perceived quality. While this scheme obtained promising experimental results, it suffered from a non-negligible loss rate, which they explained by the still large size of the used SVC layers as the transmission unit. To address this issue, in this paper we investigate reducing the granularity of transmission units down to Network Abstraction Layer (NAL) units that compose SVC layers. The proposed solution packetizes NAL units into larger chunks in an adaptive way, dynamically taking into account environmental information gathered by each node (contact times with other nodes). Simulation-driven experimental results report a 100% playout availability and a significant improvement in the received quality level with no setback in delivery times.

In this paper, a dataset of geotagged photos on a world-wide scale is presented. The dataset contains a sample of more than 14 million geotagged photos crawled from Flickr with the corresponding metadata. To guarantee the spatial representativeness of the dataset, a crawling approach based on the small-world phenomena and the Flickr friendship's graph is applied. Furthermore, the noisiness of user-provided tags is reduced through an automatic tag cleaning approach. To enable efficient retrieval, photos in the dataset are indexed based on their location information using quad-tree data structure. The dataset can assists different applications, especially, search-based automatic image annotation and reverse geotagging.

Streaming video has recently surpassed peer-to-peer networks in terms of network capacity hunger.Reports estimate a share of 40 % of peak network capacity dedicated to entertainment, mostly streaming video.A large share of this traffic originates from web-based services.YouTube alone takes up to 8 % of the prime time Internet traffic.While transmission currently works in a best effort system, multimedia information systems on the web are far from being perfect.Retrieval, annotation, validated and useful metadata, reliable and trusted services, and user interaction and context-based adaptation are still under discussion and issues to research.Currently, the Web itself faces dramatic changes, looking for example at the spread of social networks, like Facebook, Twitter or the YouTube community, which become part of everyday life of more and more people.Technologies like Linked Data, HTML5, WebM, or WebRTC are enablers that make these changes possible and let people experience the Web without knowing details about technology.Also the availability of the Web has changed a lot.Many people create multimedia data on the go, put videos and photos on microblogs, or share audiovisual data with their loved ones or colleagues.These activities also have a deep effect on multimedia data formats and usage, multimedia systems and multimedia service providers."Multimedia everywhere" was a common concept before, but this has changed to "relevant media at your finger tips", so new methods for finding, annotating, sharing, remixing, summarizing, transmitting, storing and consuming multimedia data have to be found.

With HTML5 becoming more and more standardized, it is possible to implement platform independent applications running on smartphones, tablets, laptops, and desktop PCs with different operating systems and screen resolutions. A combination of HTML5 with CSS and JavaScript allows the implementation of appealing user interfaces with advanced functionality. In this paper we present an HTML5 player for interactive non-linear videos using these technologies. In addition to standard player functions, our player provides extended navigation (button panels, table of contents, keyword search) in the video, areas for additional information, opportunities for collaboration, and a logging function. The latter allows analyses of the user behavior for further research questions. We also address first user studies.

Opportunistic networks are generally characterized by a low performance due to user mobility and non end-to-end communications, which makes it challenging to use them in scenarios involving the transmission of large data, such as video transmission. This article presents simulated experiments of transmitting different video sequences in opportunistic networks, with the goal of enhancing user video viewing experience. We consider this subjective concept objectively as a trade-off between playout delay and the delivered quality level. To obtain the best trade-off, we propose using Scalable Video Coding, which divides a video into multiple unequally important layers, providing different levels of quality. This property is exploited to transmit video layers with unequal degrees of redundancy proportionally to their degree of importance. Experimental results show that this approach enables the video to become more quickly playable at the destination with a low starting quality but progressively being enhanced, providing a better viewing-experience.

A literature review on the term "interactive video" and "interactive non-linear video" revealed different levels of interaction in varying definitions. We give a formal definition of the term "interactive non-linear video" to clarify the elements and possible relations between elements contained in such videos. Furthermore, we introduce a new event-based XML format consisting of four required and two optional elements to describe this form of video. A scene graph consisting of scenes with triggers for annotations builds the core of the format. Formal definition and XML format are both illustrated by a real world example.

A huge number of user-tagged images are daily uploaded to the web. Recently, a growing number of those images are also geotagged. These provide new opportunities for solutions to automatically tag images so that efficient image management and retrieval can be achieved. In this paper an automatic image annotation approach is proposed. It is based on a statistical model that combines two different kinds of information: high level information represented by user tags of images captured in the same location as a new unlabeled image (input image); and low level information represented by the visual similarity between the input image and the collection of geographically similar images. To maximize the number of images that are visually similar to the input image, an iterative visual matching approach is proposed and evaluated. The results show that a significant recall improvement can be achieved with an increasing number of iterations. The quality of the recommended tags has also been evaluated and an overall good performance has been observed.

A vital prerequisite for fine-grained video content processing (indexing, querying, retrieval, adaptation, etc.) is the production of accurate metadata describing its structure and semantics. Several annotation tools were presented in the literature generating metadata at different granularities (i.e. scenes, shots, frames, objects). These tools have a number of limitations with respect to the annotation of objects. Though they provide functionalities to localize and annotate an object in a frame, the propagation of this information in the next frames still requires human intervention. Furthermore, they are based on video models that lack expressiveness along the spatial and semantic dimensions. To address these shortcomings, we propose the Semantic Video Content Annotation Tool (SVCAT) for structural and high-level semantic annotation. SVCAT is a semi-automatic annotation tool compliant with the MPEG-7 standard, which produces metadata according to an object-based video content model described in this paper. In particular, the novelty of SVCAT lies in its automatic propagation of the object localization and description metadata realized by tracking their contour through the video, thus drastically alleviating the task of the annotator. Experimental results show that SVCAT provides accurate metadata to object-based applications, particularly exact contours of multiple deformable objects.

Embedded network programming remains a highly complex task for developers since unique characteristics of such networks have to be faced: one of them is the communication between a diversity of resource constraint nodes. Another one is the infrastructure dynamics. The widely-used standardized Web service technologies would perfectly meet such unique characteristics and ease the development of applications. Such technologies that enable, e.g., requesting or subscribing service data, however, process usually plain XML documents which are not suitable for small embedded devices with very limited resources. This is due to XML's verbosity, its bandwidth usage, and its associated processing overhead. The paper addresses these issues and describes an innovative and optimized source code generation technique by means of W3C's Efficient XML Interchange (EXI) format for developing XML-based Web services for the embedded domain. This offers developers a seamless use of the wide-spread service protocols in the embedded domain as well. Evaluation results based on the dataset from the ISO/IEC standardization of the vehicle to grid communication interface (V2G CI) prove the applicability of the generated XML-based Web services of restricted devices in terms of message size, performance, and code footprint.

We present the adaptation and evaluation of classification methods for TV Program recommendation. For our evaluation, we collected over a period of 10 months the TV viewing profiles of 67 users with watched 10,845 programs. Based on the results of this evaluation, we realized a TV Recommendation System.

With rapid improvements in available bandwidth and processing power of smart phones, the presentation of interactive non-linear video became possible. Web players for these form of video already exist, they usually have a video area and one or more annotation areas. Most of them can not be used for mobile phones because of their space consuming representation. This work presents a player to display interactive nonlinear videos on Android smart phones. The small display size and the possibility to switch between portrait and landscape mode have been taken into account. The player also provides functions to manage a library of downloaded interactive videos for off line use as well as bookmarks.

In this paper, an intuitive authoring system and player for interactive non-linear video called SIVA Suite is presented for demonstration. Such videos are enriched by additional content. Possible forms of additional content are plaintext, richtext, images and videos. Interactivity is implemented based upon selection buttons which allow the user to follow different plotlines. Additional forms of interactivity are realized as clickable objects in the video and a table of contents for the video. The software provides a tool for manually cutting videos and an automated shot detection. The non-linear flow of the video can be designed using a scene graph with fork nodes. Editors for text and images support the user in adding information to the video. A finished video project is exported to an XML file with a specific schema and Flash video (flv) files. The player processes the XML file, plays the interactive video and shows additional contents. It can be customized to the requirements of the presentation of the video and the corporate design of the homepage the video is embedded in.

The tremendous acceptance of Web applications, or more specifically, rich media applications are about to be extended to embedded devices such as mobile phones, digital picture frames or TV sets. The Extensible Markup Language (XML) is one important pillar when we deal with such Internet applications. XML is known as the interchange language of the Web. Besides its outstanding features, in the domain of embedded devices, XML is difficult to handle due to the processing overhead and the verbosity associated with its use. This paper evaluates the efficient access of rich Internet applications especially in the domain of resource limited embedded devices such as digital picture frames. For this purpose, typical XML source information models for rich Internet applications, such as Silverlight and SVG, are evaluated. In this context, the new efficient XML interchange (EXI) format is applied and studied. Finally a generic adaption of the EXI format is developed to even increase the efficiency. The paper concludes with the proposal for further studies on an integration of EXI-based typed interfaces to reduce the processing complexity for rich media applications on embedded devices.

Grid computing has greatly matured since the last decade. It exists now at a widely distributed scale. After 10 years of internationally combined efforts to develop from a vision to existing middlewares, we now face a large number of applications being deployed and taking benefit from the Grids. The need to handle data properly came with the applications, managing data at different semantic levels, from raw data coming from sensors in particle physics to rich data in Healthgrids. Most of the time, data management was ad hoc: The need to handle these data has been mainly seen as a constraint, and little effort has been put in their smart management in the early stages of the grid evolution. Data were present in raw files, or in databases, maybe distributed databases, but with little concerns from the application developer who focused on the core development of the process of the data. In the close past, the Grid community has been developing specialized services to handle data in a simpler and more smart way: Pieces of middlewares have been developed, for instance, to access several data sources with a common programming interface, giving the developer also the possibility of adding treatment on the data retrieved, anonymizing it, caching it, or replicating it. Works on data caching, replication, data integration, and security, to name but a few, have been seen. Efforts have been put to access and process the data, not in planning optimization or distributed balanced queries, which are core distributed database services. The database community has been investigating for a long time the issues related to the distribution of the data sources, data queries, query plan optimization in parallel systems, etc. Among the challenges rising in grid environments, we can cite, among others: dynamicity, reliability, security, data availability and transport, data indexing, search and access, autonomics, etc. These are existing distributed database challenges revisited with respect to the grid paradigm. This special issue of ‘Concurrency and Computation: Practice and Experience’ includes a selection of revised papers presented at the VLDB Data Management in Grids workshop, Vienna, Austria, held on 23rd September 2007. The 2007 edition of the workshop was the third in a raw, after the success of the 1st edition in Trondheim in 2005 and the second in Seoul in 2006. The idea of the workshop collocated with one of the most important conferences in databases (VLDB: Very Large Data Bases) is to bring together the experts from the database and grid communities, discuss, and argue the above-stated challenges. In that sense, the objective of the workshop is fulfilled, with several interesting emerging discussions during the event that may be reflected in this Special Issue. Efficient grid data management and integration is achieved only through a perfect teamwork of all levels in grid databases, i.e. the conceptual level, the middleware level for service integration, and finally the low level for data processing and redistribution. The following papers give a good overview of methods and implementations for efficient data management and integration at these levels. The paper authored by Göres and Dessloch 1 presents the PALADIN Integration Framework that allows the integrated representation of arbitrary data, metadata, and operations. On top of it different integration services can be more efficiently built, as their describing metadata have already been integrated. Rabl et al. 2 deal with the low-level aspects of data integration and management. In dynamic grid environments, automatic data management is one key issue for good performance. The paper proposes a new algorithm for load balancing which adapts the data management to the automatic scaling in the number of nodes used. The second and third papers go ‘middleware’: Kotowski et al. 3 present the GParGRES Grid database, which originally implements a middleware solution among cluster databases to enable data grid services. The architecture scales perfectly. Bassi et al. 4 present a multi-layer solution to tackle the outstanding data to be produced and managed in the forthcoming years. Their solution named Intelligent Network Caching Architecture (INCA) combines new networking entities, dedicated transport protocols, peer-to-peer middleware placement, and access functions in a comprehensive framework. Two papers dealt with replication or file management and access. In 5, Akal et al. presented a new replication scheme for grid databases following a publish/subscribe model. Principally, a middleware over the different grid nodes thus allows one to dynamically control the creation and maintenance of replicas. In 6 Hupfeld et al. analyze how an object-based file system can be adapted to Grid environment to provide an abstraction layer to the distributed file management procedure. Then they describe the XtreemFS architecture and explain how it can serve as an improvement of current data management in Grids. Santos and Koblitz present in 7 some security issues related to the management of replicated metadata catalog in the EGEE European project. They explain how security is handled in the AMGA metadata catalog, and its link and use of common security mechanisms in EGEE and Globus-based Grids (VOMS, GSI). In particular, the authors discuss the authorization policies with replicated data, illustrated with a usecase in the healthgrid field. The last paper in this special issue is authored by Norman W. Paton, invited speaker at the VLDB DMG'07 workshop. In 8, he provides an interesting position paper on the state of the art on functions already automated in the current approaches to adaptive data management (for instance, in database administration, query processing, data integration). He argues about some limitations in the current approaches of autonomic computing for data management (predictability, methodology, composability, and semantics) and highlights new challenging researches to handle automation in all aspects related to data management. These eight articles reflect the common trends of research in Grid-related projects linked with data management. We have seen a particular interest this year on adaptability and data management to make the data management in grids more sensitive to the changes in the infrastructure. We hope you will enjoy this selection of papers as much as we enjoyed them during the workshop and during the last reviewing phase. We would like to take the opportunities to thank all our Program Committee members who helped a lot during the whole process as well as G. Fox for his help to make this special issue possible. Finally, we invite you to participate in the 4th edition of the workshop that will be held at VLDB in Auckland in 2008.

Abstract Database systems have been vital for all forms of data processing for a long time. In recent years, the amount of processed data has been growing dramatically, even in small projects. Nevertheless, database management systems tend to be static in terms of size and performance, which makes scaling a difficult and an expensive task. This is especially an acute problem in dynamic environments such as grid systems. On the basis of the C‐JDBC project, we developed a cluster database that supports automatic scaling in the number of nodes used. To ensure good load balancing, we distribute the tablespace according to a statistical analysis of the query history. In this paper, we will focus on the allocation algorithm. Copyright © 2008 John Wiley & Sons, Ltd.

Traditional TV provides only a very passive experience. Recent trends suggest that users would be interested in taking a more active role while watching TV. This demo paper presents the interactive television (iTV) platform, which offers a solution in giving the TV-audience a chance of active participation. The iTV platform enriches TV with additional content and interactivity, making it more attractive for the audience. In order to support multi-user and personalized access to the iTV services mobile devices are used. To provide an easy way to offer and use services an ad-hoc service architecture - the Universal Plug and Play (UPnP) architecture - has been used.

This paper introduces a new approach of approximating the selectivity of multimedia range queries. Estimating the selectivity of a range query is a pre-requisite to optimize a multimedia database query. We use the DBSCAN clustering technique for finding high density areas in the data set. Then, the selectivity is approximated with the help of a density function in combination with the volume of the query's hyper sphere. Our approach is fast and accurate which was evaluated on an image data set using the MPEG-7 scalable color descriptor. The technique is integrated with the help of the extensible optimizer architecture in the Oracle multimedia database system.

Video adaptation is an active research area aiming at delivering heterogeneous content to yet heterogeneous devices under different network conditions. This paper presents an architecture for generating variations (different versions) from methods such as video transcoding, media conversion and summarization. The work in this paper concentrates on video data and aims to show video variation supported with metadata as an approach to adaptation to enable ubiquitous access. By video products are defined and the variation factory is introduced It generates different versions of the source and an MPEG-7 metadata document. The information contained in the metadata document helps the system to identify the most appropriate version that meets the required quality of service (QoS). In addition to the implementation of the commonly used reduction methods, two novel methods, viz. object-based and segment-based variations are introduced. Our proposals are implemented and experimentally validated.

No abstract available.

No abstract available.

This work introduces a framework for adapting MPEG-4 intra- and inter-Elementary Streams and for encoding the results in an MPEG-7 stream to be used for resource adaptation on the delivery path to the user.

Content-based image retrieval is one of the current active and promising research topics in image analysis and image management. The work of transferring the results of this research to support images in DBMS has also been initiated by many researchers and commercial systems. We first illustrate the importance of using content-based operations in combination with the traditional database operations. Then, we propose an image repository model that supports content-based operations on image databases. Furthermore, we introduce our novel similarity-based operators on image tables and investigate their properties. Finally, we investigate the use of the novel content-based operators in combination with the relational operators on image and relational tables.

The many successful research results in the domain of computer vision have made similarity based data retrieval techniques a promising approach. As a result, the integration of similarity based retrieval techniques of multimedia data into DBMSs is currently an active research issue. We first illustrate the importance of similarity based operations. Then, we present our image data repository model that supports similarity based operations conveniently under an object-relational database paradigm. Furthermore, we present novel similarity based operators on image tables and study their properties. Finally, based on the properties of the operators identified, we derive algebraic rules that are useful for similarity based query optimization and we introduce a cost model for an implementation of one of the major similarity based operators.